Project Overview
It is more and more common to operate under unusual conditions and hard constraints, with unreliable network communication, low power consumption, and the necessity to perform real-time computations. Amethix's team of engineers make the unlikely possible with embedded machine learning and microcontroller (MCU) programming.
Big Brain for Small Devices
tinyML will impact almost every industry in future—retail, healthcare, agriculture, fitness, and manufacturing to name a few.P. Warden
PROBLEM SPACE
Edge computing has become a necessity for innovative organizations across various industries. Complex scenarios demand data to be collected, processed, and analyzed on the edge, posing new challenges such as ultra-low power consumption, hard real-time execution, and unreliable network conditions. To overcome these constraints, microcontrollers powered by small factor batteries are one of the few viable options. However, implementing off-the-shelf machine learning models or deep neural networks on such devices requires custom algorithm design and implementation, which is essential for organizations looking to achieve cutting-edge results.
Amethix specializes in developing cutting-edge techniques for compressing large machine learning models and optimizing their execution on compact devices such as SoC and MCU. Leveraging methodologies such as model compression, neural network pruning, and quantization, Amethix can design hardware prototypes that are both intelligent and diminutive, while still maintaining the accuracy of their cloud-based counterparts. With Amethix, size doesn't compromise performance.
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